{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "\n",
    "## Acknowledgments\n",
    "\n",
    "This Kernel (for training purposes only) is heavelly based on [Stacked Regression: Top 4% on Leaderboard](https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard) Kernel by Serigne.\n",
    "\n",
    "## Data fields\n",
    "Here is a brief version of the data description information.\n",
    "\n",
    "* <font color='blue'>SalePrice</font>: the property's sale price in dollars. This is the target variable that you're trying to predict.\n",
    "* MSSubClass: The building class\n",
    "* MSZoning: The general zoning classification\n",
    "* LotFrontage: Linear feet of street connected to property\n",
    "* LotArea: Lot size in square feet\n",
    "* Street: Type of road access\n",
    "* Alley: Type of alley access\n",
    "* LotShape: General shape of property\n",
    "* LandContour: Flatness of the property\n",
    "* Utilities: Type of utilities available\n",
    "* LotConfig: Lot configuration\n",
    "* LandSlope: Slope of property\n",
    "* Neighborhood: Physical locations within Ames city limits\n",
    "* Condition1: Proximity to main road or railroad\n",
    "* Condition2: Proximity to main road or railroad (if a second is present)\n",
    "* BldgType: Type of dwelling\n",
    "* HouseStyle: Style of dwelling\n",
    "* OverallQual: Overall material and finish quality\n",
    "* OverallCond: Overall condition rating\n",
    "* YearBuilt: Original construction date\n",
    "* YearRemodAdd: Remodel date\n",
    "* RoofStyle: Type of roof\n",
    "* RoofMatl: Roof material\n",
    "* Exterior1st: Exterior covering on house\n",
    "* Exterior2nd: Exterior covering on house (if more than one material)\n",
    "* MasVnrType: Masonry veneer type\n",
    "* MasVnrArea: Masonry veneer area in square feet\n",
    "* ExterQual: Exterior material quality\n",
    "* ExterCond: Present condition of the material on the exterior\n",
    "* Foundation: Type of foundation\n",
    "* BsmtQual: Height of the basement\n",
    "* BsmtCond: General condition of the basement\n",
    "* BsmtExposure: Walkout or garden level basement walls\n",
    "* BsmtFinType1: Quality of basement finished area\n",
    "* BsmtFinSF1: Type 1 finished square feet\n",
    "* BsmtFinType2: Quality of second finished area (if present)\n",
    "* BsmtFinSF2: Type 2 finished square feet\n",
    "* BsmtUnfSF: Unfinished square feet of basement area\n",
    "* TotalBsmtSF: Total square feet of basement area\n",
    "* Heating: Type of heating\n",
    "* HeatingQC: Heating quality and condition\n",
    "* CentralAir: Central air conditioning\n",
    "* Electrical: Electrical system\n",
    "* 1stFlrSF: First Floor square feet\n",
    "* 2ndFlrSF: Second floor square feet\n",
    "* LowQualFinSF: Low quality finished square feet (all floors)\n",
    "* GrLivArea: Above grade (ground) living area square feet\n",
    "* BsmtFullBath: Basement full bathrooms\n",
    "* BsmtHalfBath: Basement half bathrooms\n",
    "* FullBath: Full bathrooms above grade\n",
    "* HalfBath: Half baths above grade\n",
    "* Bedroom: Number of bedrooms above basement level\n",
    "* Kitchen: Number of kitchens\n",
    "* KitchenQual: Kitchen quality\n",
    "* TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
    "* Functional: Home functionality rating\n",
    "* Fireplaces: Number of fireplaces\n",
    "* FireplaceQu: Fireplace quality\n",
    "* GarageType: Garage location\n",
    "* GarageYrBlt: Year garage was built\n",
    "* GarageFinish: Interior finish of the garage\n",
    "* GarageCars: Size of garage in car capacity\n",
    "* GarageArea: Size of garage in square feet\n",
    "* GarageQual: Garage quality\n",
    "* GarageCond: Garage condition\n",
    "* PavedDrive: Paved driveway\n",
    "* WoodDeckSF: Wood deck area in square feet\n",
    "* OpenPorchSF: Open porch area in square feet\n",
    "* EnclosedPorch: Enclosed porch area in square feet\n",
    "* 3SsnPorch: Three season porch area in square feet\n",
    "* ScreenPorch: Screen porch area in square feet\n",
    "* PoolArea: Pool area in square feet\n",
    "* PoolQC: Pool quality\n",
    "* Fence: Fence quality\n",
    "* MiscFeature: Miscellaneous feature not covered in other categories\n",
    "* MiscVal: USD alue of miscellaneous feature\n",
    "* MoSold: Month Sold\n",
    "* YrSold: Year Sold\n",
    "* SaleType: Type of sale\n",
    "* SaleCondition: Condition of sale  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis preparation\n",
    "\n",
    "## Load the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "def ignore_warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = ignore_warn \n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "DATA_FOLDER = '/kaggle/input/house-prices-advanced-regression-techniques'\n",
    "train = pd.read_csv(os.path.join(DATA_FOLDER,'train.csv'))\n",
    "test = pd.read_csv(os.path.join(DATA_FOLDER,'test.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Glimpse and prepare the data\n",
    "\n",
    "We look here to the data shape and we simply set aside the data IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train data size before dropping Id feature is : (1460, 81) \n",
      "The test data size before dropping Id feature is : (1459, 80) \n",
      "\n",
      "The train data size after dropping Id feature is : (1460, 80) \n",
      "The test data size after dropping Id feature is : (1459, 79) \n"
     ]
    }
   ],
   "source": [
    "#check the numbers of samples and features\n",
    "print(\"The train data size before dropping Id feature is : {} \".format(train.shape))\n",
    "print(\"The test data size before dropping Id feature is : {} \".format(test.shape))\n",
    "\n",
    "#Save the 'Id' column\n",
    "train_ID = train['Id']\n",
    "test_ID = test['Id']\n",
    "\n",
    "#Now drop the  'Id' colum since it's unnecessary for  the prediction process.\n",
    "train.drop(\"Id\", axis = 1, inplace = True)\n",
    "test.drop(\"Id\", axis = 1, inplace = True)\n",
    "\n",
    "#check again the data size after dropping the 'Id' variable\n",
    "print(\"\\nThe train data size after dropping Id feature is : {} \".format(train.shape)) \n",
    "print(\"The test data size after dropping Id feature is : {} \".format(test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers\n",
    "\n",
    "We visualize the scatter plot of `SalePrice` (which is the target feature) vs. `GrLivArea` to spot outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x = train['GrLivArea'], y = train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two obvious outliers - with relativelly small price for a given living area. We just remove these two outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Deleting outliers\n",
    "train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n",
    "\n",
    "#Check the graphic again\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180932.92 and sigma = 79467.79\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-transform of the target variable\n",
    "\n",
    "We correct the distribution of the target variable by applying log1p transform to all values. After prediction, we apply back this transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#Check the new distribution \n",
    "sns.distplot(train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features engineering\n",
    "\n",
    "We apply features engineering transform to all data.\n",
    "\n",
    "We concatenate train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data size is : (2917, 79)\n"
     ]
    }
   ],
   "source": [
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "y_train = train.SalePrice.values\n",
    "all_data = pd.concat((train, test)).reset_index(drop=True)\n",
    "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
    "print(\"all_data size is : {}\".format(all_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing data\n",
    "\n",
    "We show the distribution of the missing data. We order features by percent of missing data and we show the 20 features with largest percent of missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>99.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>96.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>93.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>80.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>48.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>16.661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>5.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>5.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>5.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>5.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>5.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>2.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>2.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>2.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>2.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>2.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Ratio\n",
       "PoolQC               99.691\n",
       "MiscFeature          96.400\n",
       "Alley                93.212\n",
       "Fence                80.425\n",
       "FireplaceQu          48.680\n",
       "LotFrontage          16.661\n",
       "GarageQual            5.451\n",
       "GarageCond            5.451\n",
       "GarageFinish          5.451\n",
       "GarageYrBlt           5.451\n",
       "GarageType            5.382\n",
       "BsmtExposure          2.811\n",
       "BsmtCond              2.811\n",
       "BsmtQual              2.777\n",
       "BsmtFinType2          2.743\n",
       "BsmtFinType1          2.708\n",
       "MasVnrType            0.823\n",
       "MasVnrArea            0.788\n",
       "MSZoning              0.137\n",
       "BsmtFullBath          0.069\n",
       "BsmtHalfBath          0.069\n",
       "Utilities             0.069\n",
       "Functional            0.069\n",
       "Electrical            0.034\n",
       "BsmtUnfSF             0.034\n",
       "Exterior1st           0.034\n",
       "Exterior2nd           0.034\n",
       "TotalBsmtSF           0.034\n",
       "GarageArea            0.034\n",
       "GarageCars            0.034"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\n",
    "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also plot this distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent missing data by feature')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10,8))\n",
    "plt.xticks(rotation='90')\n",
    "sns.barplot(x=all_data_na.index, y=all_data_na)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Percent of missing values', fontsize=15)\n",
    "plt.title('Percent missing data by feature', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also look to the correlation map to see which features are mostly correlated with `SalesPrice`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feb06947470>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation map to see how features are correlated with SalePrice\n",
    "corrmat = train.corr()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing missing variables\n",
    "\n",
    "We fill with `None` all missing `PoolQC` since missing data means most probably that there is no Pool. Most of the properties will not have a Pool so the assumption is most probably correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same treatment for `MiscFeature` - missing data means that there are no miscelaneous features present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have missing data for `Alley`, we reckon that there is no alley so again we fill it with None. Same treatment for `Fence` and `FireplaceQu` (quality)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")\n",
    "all_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")\n",
    "all_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dimmension of the `LotFrontage`, we suppose that on the same neighborhood we will have similar values for `LotFrontage` therefore we fill with the median value for the neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood\n",
    "all_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n",
    "    lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For garages, we fill with `None` missing data, because we suppose that missing data means no garrage present. We apply this raationale for both the categorial and numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n",
    "    all_data[col] = all_data[col].fillna('None')\n",
    "    \n",
    "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "    all_data[col] = all_data[col].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same rationale for basement related features. We fill with 0 the values for numerical features (because missing values means probably missing basement and therefore we can agree all these values ore 0). For categorical features, we fill all missing with None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
    "    all_data[col] = all_data[col].fillna(0)\n",
    "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "    all_data[col] = all_data[col].fillna('None')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For masonry type and area we fill with `None` and 0 respectivelly for missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
    "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill with mode all missing data for Zoning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop `Utilities` feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.drop(['Utilities'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill all missing data for `Functional` with `Typ`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use mean imputation for missing `Electrical` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use mode imputation for missing `KitchenQual` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same for `Exterior1st`, `Exterior2nd`, `SaleType`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
    "all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill with 0 the missing values for `MSSubClass` - the value of building class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check now if there are any other missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Ratio]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
    "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
    "missing_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We treated all missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  More features engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set as string - to be treated as categorical values few of the features:\n",
    "\n",
    "* Building class;  \n",
    "* Overall condition;  \n",
    "* Year of the transaction;    \n",
    "* Month of the transaction;  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MSSubClass=The building class\n",
    "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n",
    "\n",
    "\n",
    "#Changing OverallCond into a categorical variable\n",
    "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
    "\n",
    "\n",
    "#Year and month sold are transformed into categorical features.\n",
    "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
    "all_data['MoSold'] = all_data['MoSold'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label encoding few variables\n",
    "\n",
    "We apply label encoding for few of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape all_data: (2917, 78)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond', \n",
    "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1', \n",
    "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
    "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond', \n",
    "        'YrSold', 'MoSold')\n",
    "# process columns, apply LabelEncoder to categorical features\n",
    "for c in cols:\n",
    "    lbl = LabelEncoder() \n",
    "    lbl.fit(list(all_data[c].values)) \n",
    "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
    "\n",
    "# shape        \n",
    "print('Shape all_data: {}'.format(all_data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1stFlrSF', '2ndFlrSF', '3SsnPorch', 'Alley', 'BedroomAbvGr',\n",
       "       'BldgType', 'BsmtCond', 'BsmtExposure', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtFinType1', 'BsmtFinType2', 'BsmtFullBath', 'BsmtHalfBath',\n",
       "       'BsmtQual', 'BsmtUnfSF', 'CentralAir', 'Condition1', 'Condition2',\n",
       "       'Electrical', 'EnclosedPorch', 'ExterCond', 'ExterQual', 'Exterior1st',\n",
       "       'Exterior2nd', 'Fence', 'FireplaceQu', 'Fireplaces', 'Foundation',\n",
       "       'FullBath', 'Functional', 'GarageArea', 'GarageCars', 'GarageCond',\n",
       "       'GarageFinish', 'GarageQual', 'GarageType', 'GarageYrBlt', 'GrLivArea',\n",
       "       'HalfBath', 'Heating', 'HeatingQC', 'HouseStyle', 'KitchenAbvGr',\n",
       "       'KitchenQual', 'LandContour', 'LandSlope', 'LotArea', 'LotConfig',\n",
       "       'LotFrontage', 'LotShape', 'LowQualFinSF', 'MSSubClass', 'MSZoning',\n",
       "       'MasVnrArea', 'MasVnrType', 'MiscFeature', 'MiscVal', 'MoSold',\n",
       "       'Neighborhood', 'OpenPorchSF', 'OverallCond', 'OverallQual',\n",
       "       'PavedDrive', 'PoolArea', 'PoolQC', 'RoofMatl', 'RoofStyle',\n",
       "       'SaleCondition', 'SaleType', 'ScreenPorch', 'Street', 'TotRmsAbvGrd',\n",
       "       'TotalBsmtSF', 'WoodDeckSF', 'YearBuilt', 'YearRemodAdd', 'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add Total SqFt feature\n",
    "\n",
    "We add an additional features, for total area of the house."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding total sqfootage feature \n",
    "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 0.125\n",
    "# Adding total above ground \n",
    "all_data['TotalAboveGroundSF'] = all_data['1stFlrSF'] + all_data['2ndFlrSF']\n",
    "# All bathrooms\n",
    "all_data['AllBath'] = all_data['FullBath'] + all_data['HalfBath']\n",
    "all_data['BsmtAllBath'] = all_data['BsmtFullBath'] + all_data['BsmtHalfBath']\n",
    "all_data['FullAllBath'] = all_data['AllBath'] + all_data['BsmtAllBath']\n",
    "# Average Bedroom\n",
    "all_data['AverageBedroomSF'] = all_data['TotalAboveGroundSF'] / (all_data['BedroomAbvGr'] + eps)\n",
    "# Bedroom / Bathroom ratio\n",
    "all_data['BedroomBathroomRatio'] = all_data['BedroomAbvGr'] / (all_data['AllBath'] + eps)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_agg_values(df, group, feature, criteria):\n",
    "    temp = df.groupby(group)[feature].agg([criteria]).rename({criteria:f'{feature}_{group}_{criteria}'},axis=1)\n",
    "    df = pd.merge(df,temp,on=group,how='left')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = build_agg_values(all_data, 'BedroomAbvGr', 'TotalAboveGroundSF', 'mean')\n",
    "all_data = build_agg_values(all_data, 'AllBath', 'TotalAboveGroundSF', 'mean')\n",
    "all_data = build_agg_values(all_data, 'Neighborhood', 'AllBath', 'mean')\n",
    "all_data = build_agg_values(all_data, 'Neighborhood', '1stFlrSF', 'mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skewed features\n",
    "\n",
    "\n",
    "For the skewed features (numerical features with skewed distribution of the values) we want to apply a log1p transformation.  \n",
    "\n",
    "\n",
    "First, we detect these skewed features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skew in numerical features: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>21.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageBedroomSF</th>\n",
       "      <td>17.689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>17.689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>13.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>12.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>11.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>4.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>4.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>4.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>4.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Skew\n",
       "MiscVal          21.940\n",
       "AverageBedroomSF 17.689\n",
       "PoolArea         17.689\n",
       "LotArea          13.109\n",
       "LowQualFinSF     12.085\n",
       "3SsnPorch        11.372\n",
       "LandSlope         4.973\n",
       "KitchenAbvGr      4.301\n",
       "BsmtFinSF2        4.145\n",
       "EnclosedPorch     4.002"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
    "\n",
    "# Check the skew of all numerical features\n",
    "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "print(\"\\nSkew in numerical features: \\n\")\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the skeweness factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent missing data by feature')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(14, 10))\n",
    "plt.xticks(rotation='90')\n",
    "sns.barplot(x=skewness.index, y=skewness.Skew)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Percent of missing values', fontsize=15)\n",
    "plt.title('Percent missing data by feature', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply BoxCox transform to skewed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 69 skewed numerical features to Box Cox transform\n"
     ]
    }
   ],
   "source": [
    "skewness = skewness[abs(skewness) > 0.75]\n",
    "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
    "\n",
    "from scipy.special import boxcox1p\n",
    "skewed_features = skewness.index\n",
    "lam = 0.15\n",
    "for feat in skewed_features:\n",
    "    all_data[feat] = boxcox1p(all_data[feat], lam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Dummy categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2917, 230)\n"
     ]
    }
   ],
   "source": [
    "all_data = pd.get_dummies(all_data)\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set back the train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = all_data[:ntrain]\n",
    "test = all_data[ntrain:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso is very sensitive to outliers. We apply a RobustScaler before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ElasticNet is very sensitive to outliers as well. We apply a RobustScaler before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "                             learning_rate=0.05, max_depth=3, \n",
    "                             min_child_weight=1.7817, n_estimators=2200,\n",
    "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "                             subsample=0.5213, silent=1,\n",
    "                             random_state =7, nthread = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Validation function\n",
    "n_folds = 5\n",
    "\n",
    "def rmsle_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lasso score: 0.1121 (0.0075)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lasso_score = rmsle_cv(lasso)\n",
    "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(lasso_score.mean(), lasso_score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet score: 0.1121 (0.0075)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "enet_score = rmsle_cv(ENet)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(enet_score.mean(), enet_score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel Ridge score: 0.1158 (0.0072)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kr_score = rmsle_cv(KRR)\n",
    "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(kr_score.mean(), kr_score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting score: 0.1164 (0.0074)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gb_score = rmsle_cv(GBoost)\n",
    "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(gb_score.mean(), gb_score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xgboost score: 0.1152 (0.0065)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_score = rmsle_cv(model_xgb)\n",
    "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(xgb_score.mean(), xgb_score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM score: 0.1152 (0.0070)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lgb_score = rmsle_cv(model_lgb)\n",
    "print(\"LGBM score: {:.4f} ({:.4f})\\n\" .format(lgb_score.mean(), lgb_score.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble models\n",
    "\n",
    "### Average model\n",
    "\n",
    "We average the prediction of several models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        \n",
    "    # we define clones of the original models to fit the data in\n",
    "    def fit(self, X, y):\n",
    "        self.models_ = [clone(x) for x in self.models]\n",
    "        \n",
    "        # Train cloned base models\n",
    "        for model in self.models_:\n",
    "            model.fit(X, y)\n",
    "\n",
    "        return self\n",
    "    \n",
    "    #Now we do the predictions for cloned models and average them\n",
    "    def predict(self, X):\n",
    "        predictions = np.column_stack([\n",
    "            model.predict(X) for model in self.models_\n",
    "        ])\n",
    "        return np.mean(predictions, axis=1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Averaged base models score: 0.1092 (0.0075)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso))\n",
    "\n",
    "score = rmsle_cv(averaged_models)\n",
    "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07898572646253181\n"
     ]
    }
   ],
   "source": [
    "averaged_models.fit(train.values, y_train)\n",
    "averaged_models_train_pred = averaged_models.predict(train.values)\n",
    "averaged_pred = np.expm1(averaged_models.predict(test.values))\n",
    "print(rmsle(y_train, averaged_models_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submission with the prediction of average models (Elastic Net, Gradient Boosting, Kernel Ridge and Lasso)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame()\n",
    "sub['Id'] = test_ID\n",
    "sub['SalePrice'] = averaged_pred\n",
    "sub.to_csv('averaged_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07748083754426906\n"
     ]
    }
   ],
   "source": [
    "model_xgb.fit(train, y_train)\n",
    "xgb_train_pred = model_xgb.predict(train)\n",
    "xgb_pred = np.expm1(model_xgb.predict(test))\n",
    "print(rmsle(y_train, xgb_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07141995190277185\n"
     ]
    }
   ],
   "source": [
    "model_lgb.fit(train, y_train)\n",
    "lgb_train_pred = model_lgb.predict(train)\n",
    "lgb_pred = np.expm1(model_lgb.predict(test.values))\n",
    "print(rmsle(y_train, lgb_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_average = averaged_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submission with the weighted average of the simple average of 4 models predictions and prediction of XGB and LGB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame()\n",
    "sub['Id'] = test_ID\n",
    "sub['SalePrice'] = weighted_average\n",
    "sub.to_csv('weighted_average_submission.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
